Multiple Variables Research Articles

Machine learning based graphical interface for accurate estimation of FRP-concrete bond strength under diverse exposure conditions

Predicting FRP-to-concrete bond strength (FRP-CBS) under diverse exposure conditions is an intricate task influenced by multiple variables. Yet, existing pertinent models have several limitations. Accordingly, this study proposes a novel data driven machine learning (ML) methodology to predict the FRP-CBS considering various exposure conditions. A comprehensive database on single and double lap-shear strength tests on concrete specimens was meticulously compiled. Twenty-seven analytical models were used to appraise the developed ML models. Feature importance analysis was conducted to ascertain the influence of input parameters on bond strength. The proposed data-driven ML models attained exceptional accuracy and superior performance compared to existing analytical models. To enhance the accuracy of bond strength estimation and simplify the process for practicing engineers and FRP applicators, a user-friendly graphical interface was developed. It could eliminate the need for complex design procedures, making it easier to accurately estimate the FRP-CBS, thus improving overall efficiency in engineering practice.

Performance assessment for climate intervention (PACI): preliminary application to a stratospheric aerosol injection scenario

As the prospect of exceeding global temperature targets set forth in the Paris Agreement becomes more likely, methods of climate intervention are increasingly being explored. With this increased interest there is a need for an assessment process to understand the range of impacts across different scenarios against a set of performance goals in order to support policy decisions. The methodology and tools developed for Performance Assessment (PA) for nuclear waste repositories shares many similarities with the needs and requirements for a framework for climate intervention. Using PA, we outline and test an evaluation framework for climate intervention, called Performance Assessment for Climate Intervention (PACI) with a focus on Stratospheric Aerosol Injection (SAI). We define a set of key technical components for the example PACI framework which include identifying performance goals, the extent of the system, and identifying which features, events, and processes are relevant and impactful to calculating model output for the system given the performance goals. Having identified a set of performance goals, the performance of the system, including uncertainty, can then be evaluated against these goals. Using the Geoengineering Large Ensemble (GLENS) scenario, we develop a set of performance goals for monthly temperature, precipitation, drought index, soil water, solar flux, and surface runoff. The assessment assumes that targets may be framed in the context of risk-risk via a risk ratio, or the ratio of the risk of exceeding the performance goal for the SAI scenario against the risk of exceeding the performance goal for the emissions scenario. From regional responses, across multiple climate variables, it is then possible to assess which pathway carries lower risk relative to the goals. The assessment is not comprehensive but rather a demonstration of the evaluation of an SAI scenario. Future work is needed to develop a more complete assessment that would provide additional simulations to cover parametric and aleatory uncertainty and enable a deeper understanding of impacts, informed scenario selection, and allow further refinements to the approach.

Electrochemical Reduction of Ytterbium in Non-Aqueous Solvents - Towards Production of Pure Medical Lutetium-177

In recent years targeted radionuclide therapy has become a topic of great interest as a growing number of cancers can potentially be treated with radionuclides like lutetium-177 and samarium-153. To facilitate this growth and meet the demand, preparation and purification of lutetium-177 is increasing in importance. In the most common production route, enriched ytterbium-176 targets are irradiated and the produced lutetium-177 is separated from the ytterbium target material. This challenging separation is commonly performed using ion exchange resins, a method limited in scalability as the chemical similarity between ytterbium and lutetium results in poor separation at higher concentrations.An alternative method to aid the separation of lutetium from ytterbium is the electrochemical reduction of ytterbium as a change in oxidation states changes chemical properties significantly. Ytterbium has an accessible divalent state, whereas lutetium has not. However, the very negative value of the Yb(III)/Yb(II) standard reduction potential (E0= -1.04 V vs. SHE) does pose some challenges as it becomes a necessity to work in non-aqueous solvents with a very low water content. Similar research has already been performed in our group, looking into the separation of samarium-153 from europium-154, reducing europium in nitrate media, followed by a separation over TEVA resin. In this study, we investigated the reduction of ytterbium in ethylene glycol, dimethyl sulfoxide, tetrahydrofuran and N,N-dimethyl formamide by cyclic voltammetry, linear sweep voltammetry and electronic impedance spectroscopy. The influence of multiple variables such as moisture content on the reduction kinetics and stability of Yb(II) was determined and a Koutecky-Levich analysis was performed. Our results demonstrate that the reduction of ytterbium in non-aqueous solvents is feasible but attempts to perform bulk electrolysis were not successful yet. Ethylene glycol, dimethyl sulfoxide and N,N-dimethyl formamide demonstrate favorable conditions and in each solvents clear reduction and oxidation peaks were observed. It was found that the solvents do affect the peak separation, reduction potential and sensitivity to passivation. Figure 1

Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete

In recent years, the construction industry has been striving to make production faster and handle more complex architectural designs. Waste reduction, geometric freedom, lower construction costs, and speedy construction make the 3D-printed fiber-reinforced concrete (3DPFRC) alternative for future construction. However, achieving the optimum mixture composition for 3DPFRC remains a daunting task, entailing the consideration of multiple variables and necessitating an extensive trial-and-error experimental process. Therefore, this study investigated the application of different metaheuristic optimization algorithms to predict the compressive strength (CS) of 3DPFRC. A database of 299 data samples with 16 different input features was compiled from the experimental studies in the literature. Six metaheuristic algorithms, such as human felicity algorithm (HFA), differential evolution algorithm (DEA), nuclear reaction optimization (NRO), Harris hawks optimization (HHO), lightning search algorithm (LSA), and tunicate swarm algorithm (TSA) were applied to identify the optimal hyperparameter combination for the random forest (RF) model in predicting the CS of 3DPFRC. Different statistical metrics and 10-fold cross-validation were used to evaluate the accuracy of the models. The TSA-RF model exhibited superior performance compared to other models, achieving correlation (R), mean absolute error (MAE), and root mean square error (RMSE) values of 0.99, 2.10 MPa, and 3.59 MPa, respectively. The LSA-RF model also performed well, with R, MAE, and RMSE values of 0.99, 2.93 MPa, and 6.23 MPa, respectively. SHapley Additive exPlanation (SHAP) interpretability elucidates the intricate relationships between features and their effects on the CS, thereby offering invaluable insights for the performance-based mix proportion design of 3DPFRC.

A novel fractional system grey prediction model with dynamic delay effect for evaluating the state of health of lithium battery

Accurately predicting the state of health (SOH) of lithium batteries is critical to improving the energy storage technology of batteries. However, most research focuses solely on the performance degradation trends of lithium batteries during cycling currently, while ignoring the dynamic time lag effects of the influencing factors. Therefore, a novel fractional system grey prediction model with dynamic delay effect is proposed in this paper (abbreviated as FHMGM(1, N)). The fractional-order Huasdorff operator is introduced into the grey prediction model to reflect the dynamic delay between variables in the process of charging and discharging. Meanwhile, the selected grey prediction model with system structure can better characterize the relationships among multiple variables in the system and analyze several main variables that have parallel relationships. In addition, using genetic algorithm to determine the optimal nonlinear parameters can effectively improve the accuracy and stability of the model in the prediction process. Finally, the new model is applied to two types of lithium battery datasets for fitting and prediction, which are compared with the existing grey models and artificial intelligence models. The results show that the new model can better adapt to lithium battery data with different structures, showing good robustness and prediction performance.

THE ROLE OF OUTDOOR LIGHT EXPOSURE ON SLEEP QUALITY IN OLDER INDIVIDUALS WITH FRAILTY OF DIFFERENT LIVING TYPES

Abstract The study investigates the relationship between outdoor light exposure and sleep quality in older individuals with frailty in institutions and the community, where sleep disorders are common, and outdoor light exposure is inadequate.The study includes older individuals with frailty in community and institutions in Taiwan. Participants must be aged 65 or above and have a Clinical Frailty Scale score between 4 to 8. We collect sleep quality and outdoor light exposure data using questionnaires and actigraphy.The study includes 77 older individuals: 29 from day-care centers, 22 from conventional long-term care institutions, and 26 from unit care institutions. The average outdoor light exposure is 15 minutes per day, with an average Pittsburgh Sleep Quality Index (PSQI) score of 8, 74% sleep efficiency, and 125.8 minutes of wake after sleep onset. After controlling for multiple variables. Outdoor light exposure shows no relationship with PSQI, sleep efficiency, and wake after sleep onset.The average PSQI score is > 5, indicating poor subjective sleep quality. Additionally, Wake After Sleep Onset > 60 minutes, and sleep efficiency < 75%, suggesting poor objective sleep quality. The average outdoor light exposure time < 20 minutes, indicating a lack of outdoor light exposure. However, our study does not observe the effects of light exposure, possibly because the average daily light exposure is below the 30-minutes threshold. Older individuals with frailty experience poor sleep quality. It is possible that a longer duration of outdoor light exposure may be needed to observe its impact on sleep quality.

EvoLaps 2: Advanced phylogeographic visualization.

EvoLaps is a user-friendly web application designed to visualize the spatial and temporal spread of pathogens. It takes an annotated tree as entry, such as a maximum clade credibility tree obtained through continuous phylogeographic inference. By following a 'Top-Down' reading of a tree recursively, transitions (latitude/longitude changes from a node to its children) are represented on a cartographic background using graphical paths. The complete set of paths forms the phylogeographic scenario. EvoLaps offers several features to analyze complex scenarios: (1) enhanced path display using multiple graphical variables with time-dependent gradients, (2) cross-highlighting and selection capabilities between the phylogeographic scenario and the phylogenetic tree, (3) production of specific spatio-temporal scales and synthetic views through dynamic and iterative clustering of localities into spatial clusters, (4) animation of the phylogeographic scenario using tree brushing, which can be done manually or automatically, gradually over time or at specific time intervals, and for the entire tree or a specific clade, and (5) an evolving library of additional tools. EvoLaps is freely available for use at evolaps.org.

Realistic assessment of tire and road wear particle emissions and their influencing factors on different types of roads

This study aims to examine tire and road wear particle (TRWP) emissions under realistic conditions in order to provide some valuable insights into understanding their sources and fate in the environment. TRWP emissions were evaluated with a fully instrumented vehicle driving on five representative road types: urban, ring road, suburban, highway, and rural. Multiple vehicle dynamic variables were recorded to assess the factors influencing these emissions. For the first time, emitted particles were collected on filters and analyzed by means of pyrolysis coupled with gas chromatography-mass spectrometry to determine the polymeric content of tires, in specifically quantifying styrene-butadiene rubber (SBR) and butadiene rubber (BR) pyrolytic markers. The measurements obtained from the five road types revealed similar size distributions for SBR + BR emissions, with maxima found in the (ultra)fine fraction (< 0.39 µm). Upon applying an SBR + BR-to-TRWP conversion factor, (ultra)fine fraction TRWP emissions proved to be the highest for suburban (64 ± 5 µg/km), followed by highway, urban, ring road and rural routes. The output represents up to 480 tons of TRWP per year emitted in the EU27, thus suggesting a widely impregnated atmospheric compartment capable of threatening human health. Furthermore, an analysis of variables revealed that acceleration, tire constraints, and constant sustained driving factors had specific impacts on TRWP emissions.

Validation of Constant Work Rate Cycling Endurance Time for Use in Chronic Obstructive Pulmonary Disease Clinical Trials.

Rationale: A COPD Foundation working group sought to identify measures of exercise endurance, a meaningful aspect of physical functioning in everyday life among patients with chronic obstructive pulmonary disease (COPD) that is not fully accepted in regulatory decision making, hampering drug development. Objectives: To demonstrate, as we previously asserted (Casaburi COPD 2022;9:252), that constant work rate cycling endurance time is an appropriate exercise endurance measure in patients with COPD. Methods: To validate this assertion, we assembled an integrated database of endurance time responses, including 8 bronchodilator (2,166 subjects) and 15 exercise training (3,488 subjects) studies (Casaburi COPD 2022;9:520). Results: Construct validity was demonstrated: 1) peak physiologic and perceptual responses were similar for constant work rate and incremental cycling; 2) after bronchodilator therapy, there were greater increases in endurance time in patients with more severe airflow limitation; 3) after exercise training, endurance time increases were similar across airflow limitation severities; and 4) there were correlations between changes in endurance time and changes in mechanistically related physiologic and perceptual variables. Test-retest reliability was demonstrated, with consistency of changes in endurance time at two time points after the intervention. Responsiveness was confirmed, with significant increases in endurance time after active (but not placebo) bronchodilator therapy, with greater increases seen with more severe airflow limitation and after exercise training. On the basis of regression analysis using multiple anchor variables, the minimum important difference for endurance time increase is estimated to be approximately 1 minute. Conclusions: Constant work rate cycling endurance time is a valid exercise endurance measure in COPD, suitable for contributing to the evaluation of treatment benefit supporting regulatory decision making and evidence-based therapeutic recommendations.

Determinants of Media Consumption by Audience – Creating a Conceptual Model

Purpose: This paper aims to comprehend the various factors that influence the audience's media consumption so that media firms may create content effectively. Design: Newspaper articles, academic journals, case studies, and online searches were the main sources of information used in this study. The study evaluates the postulates and variables that impact media consumption through focus group interactions. Findings: The study thoroughly reviews empirical concepts to examine the postulates influencing media consumption. The variables related to the study were perception, likes and preferences, awareness, local media services, and actions that affect media consumption, as determined by developing a conceptual model evaluation. Originality/value: While the conceptual model was being developed, the study discussed multiple variables that influence media consumption and, with the aid of several models and theories linked to media consumption, identified several postulates. Paper type: A literature review to create the conceptual framework.

Deep learning-based vibration stress and fatigue-life prediction of a battery-pack system

The primary concerns of the automotive industry are structural integrity and battery-pack system (BPS) reliability. To ascertain the appropriate thickness of critical BPS components (e.g., the long bracket, crossbeam, and bottom shell), engineers are required to perform several simulations of finite element (FE) analysis. This procedure is laborious and requires a significant amount of time. This study introduces a very effective approach for predicting vibration-induced stress and fatigue life, intending to enhance dependability in the design process. Firstly, a three-layer lithium battery model is utilized to investigate the impact of vibration on the maximal Mises stress at various states of charge. This information is used to model the BPS, including the batteries. The nonlinear FE model of the BPS is validated using the modal test results. Secondly, a sensitivity analysis is performed to ascertain the impact of component thickness on the maximum Mises stress. For vibration simulations, multiple design variables are selected to collect data. Next, the nonlinear relationship between inputs (thicknesses of critical components) and outputs (maximum Mises stress and minimum fatigue life) is described using a deep learning (DL) modeling framework with forward and backward propagation. Finally, the accuracy of the DL model is assessed by measuring error functions and comparing its performance to six commonly employed methods. Furthermore, the inclusion of Gaussian noise is employed to assess the model’s robustness and ability to generalize. Additionally, the establishment of fatigue-life and stress boundaries serves to offer designers valuable insights. The results indicate that the proposed method for predicting vibration stress and fatigue life is highly efficient and cost-effective, making it useful for the design of a robust and reliable BPS.

Correlates of frailty in older female cancer survivors

IntroductionCancer survivors are at risk of frailty because of cancer and its treatment. Understanding the factors that increase the risk of frailty is an important aspect of cancer care for the development of interventions to prevent or manage frailty, thus improving cancer survival and overall quality of life of cancer survivors. This study aimed to identify demographic, clinical, and psychosocial correlates of frailty in older, female cancer survivors. Materials and MethodsThis is a sub-study focusing on the exploratory aim of a larger cross-sectional study (NURS-IIR-IUSCC-0748). A total of 213 female cancer survivors aged 59–87 years old were included from the parent study in the current analysis. Frailty, the primary outcome, was measured using the Tilburg Frailty Indicator scale. The independent variables were age, relationship status, clinical stage of cancer, treatment type, comorbidity, depression, affect, optimism, stress, and social support. Stepwise linear regression modeling identified the independent variables that were significantly associated with frailty. ResultsThe final regression model revealed that high patient-reported stress and depression, comorbidity, not being married or living with a partner, and low positive affect were significantly associated with worsening frailty in this population. DiscussionUnderstanding the context of frailty is important for the design of interventions that target factors known to be associated with frailty in older cancer survivors. Further validation with a larger and a more diverse sample from a broad spectrum of sociodemographic and clinical population would fully account for the multiple independent variables influencing frailty in cancer survivors.

Effects of Chinese heart-healthy diet on blood lipids, glucose, and estimated 10-y cardiovascular disease risk among Chinese adults: results on secondary outcomes of a randomized controlled trial

BackgroundHealthy diet is essential for cardiovascular disease risk management, but its effects among Chinese patients, whose diets differ from Western diets, remain largely unknown. MethodsIn this multicenter, patient- and outcome assessor-blind, randomized controlled feeding trial, 265 Chinese adults with baseline systolic blood pressure 130 to 159 mmHg were randomly assigned into Chinese heart-healthy (CHH) diet or usual diet for a 28-d intervention after a 7-d run-in period on usual diet. Blood lipids and glucose were measured from overnight fasting blood samples before and after the intervention. Ten-year cardiovascular disease risk was estimated using models previously developed and validated in Chinese. The changes in secondary outcomes of serum total cholesterol (TC), blood glucose, and 10-y cardiovascular disease risk over the intervention period were compared between intervention groups, adjusting for center, among participants with baseline and follow-up blood samples available. Sensitivity analyses were done with further adjustment for baseline values and covariables; missing data imputed; and among per-protocol population. ResultsAmong 256 eligible participants (130 on CHH diet, 126 on control diet), 42% had hypercholesterolemia and 15% had diabetes at baseline. In the control group, TC and 10-y cardiovascular disease risk decreased after the intervention by 0.16 mmol/L and 0.91%, respectively, but blood glucose increased by 0.25 mmol/L. Compared with usual diet, the CHH diet lowered TC (−0.14 mmol/L, P = 0.017) and 10-y cardiovascular disease risk (−1.24%, P = 0.001) further. No effect on blood glucose was found. All sensitivity analyses confirmed the results on TC and 10-y cardiovascular disease risk, and analysis with multiple variables adjusted showed a borderline significant effect on blood glucose (−0.17 mmol/L, P = 0.051). The differences in intake of nutrients and food groups between intervention groups explained the results. ConclusionsThe CHH diet reduced TC and 10-y cardiovascular disease risk and was likely to reduce blood glucose among Chinese adults with mild hypertension. Further studies with longer terms are warranted.This trial was registered at clinicaltrials.gov as NCT03882645.

Exploring cause-specific strategies for suicide prevention in India: A multivariate VARMA approach

Efficiently predicting suicide rates aids resource allocation and response preparedness. This study investigates time-series data with multiple variables to model and forecast suicide events in India. Utilizing official suicide statistics (2001–2021), results highlight the superiority of the multivariate VARMA model over VAR and univariate ARIMA models. This approach uncovers overlooked patterns and a concerning upward trend in future Indian suicide incidents. The research provides insights that aid public health professionals in targeting high-need areas and enhancing readiness and suggests cause-specific preventive strategies to counter this trend.

Evidence of subgroup differences in meta-analyses evaluating medications for alcohol use disorder: An umbrella review.

Randomized controlled trials (RCTs) evaluating medications for alcohol use disorder (AUD) often examine heterogeneity of treatment effects through subgroup analyses that contrast effect estimates in groups of patients across individual demographic, clinical, and study design-related characteristics. However, these analyses are often not prespecified or adequately powered, highlighting the potential role of subgroup analyses in meta-analysis. Here, we conducted an umbrella review (i.e., a systematic review of meta-analyses) to determine the range and characteristics of reported subgroup analyses in meta-analyses of AUD medications. We searched PubMed to identify meta-analyses of RCTs evaluating medications for the management of AUD, alcohol abuse, or alcohol dependence in adults. We sought studies that measured drinking-related outcomes; quality of life, function, and rates of mortality; adverse events; and dropout. We considered meta-analyses that reported the results from formal subgroup analyses (comparing the summary effects across subgroup levels); summary effect estimates stratified across subgroup levels; and meta-regression, regression, or correlation-based subgroup analyses. We analyzed nine meta-analyses that included 61 formal subgroup analyses (median = 6 per meta-analysis), of which 33 (54%) were based on baseline participant-level and 28 (46%) were based on trial-level characteristics. Of the 58 subgroup analyses with either a p-value from a subgroup test or a statement by the authors that the subgroup analyses were not statistically significant, eight (14%) were statistically significant at the p < 0.05 level. Twelve meta-analyses reported the results of 102 meta-regression analyses, of which 25 (25%) identified statistically significant predictors of the relevant outcome of interest; nine (9%) were based on baseline participant-level and 93 (91%) were based on trial characteristics. Subgroup analyses across meta-analyses of AUD medications often focus on study-level characteristics, which may not be as clinically informative as subgroup analyses based on participant-level characteristics. Opportunities exist for future meta-analyses to standardize their subgroup methodology, focus on more clinically informative participant-level characteristics, and use predictive approaches to account for multiple relevant variables.

개인특성, 가정 및 학교환경 변인이 고등학생의 진로적응력에 미치는 영향

Objectives This study examined the influence of personal characteristics(self-esteem, social withdrawal, de-pression, academic engagement, and cooperation), family environment(parenting attitudes), and school environ-ment(friendship and teacher relationships) on predictors of career adaptability in high school students.&#x0D; Methods For this purpose, hierarchical regression analysis was performed on the high school data of 1,213 boys and 1,041 girls from the Korean Children and Youth Panel Survey 2018. Hierarchical regression analysis is a step-by-step method of determining the magnitude of the relative influence of multiple independent variables on the dependent variable.&#x0D; Results The results of this study were as follows. First, the factors that influenced high school students' career adaptability were academic engagement and self-esteem, inconsistent parenting, social withdrawal and depres-sion, and friendships. Second, when career adaptability was analyzed by dividing it into career interest, career control, career curiosity, and career confidence, the personal characteristics of academic engagement was found to be the most influential variable. In addition, it was also found that personal characteristics, family environment, and school environment have multi-angular effects on career adaptability and career interest, but school environ-ment has no effect on career control, career curiosity, and career confidence.&#x0D; Conclusions Based on this result, implications of this study and suggestions for improving the career adaptability of high school students were discussed.

Usefulness of Age, Creatinine and Ejection Fraction - Modification of Diet in Renal Disease Score for Predicting Survival in Patients with Heart Failure.

Central Illustration: Usefulness of Age, Creatinine and Ejection Fraction - Modification of Diet in Renal Disease Score for Predicting Survival in Patients with Heart Failure Summary of the study design and key findings. ACEF: Age, creatinine and ejection fraction, MDRD: Modified Diet in Renal Disease. While many risk models have been developed to predict prognosis in heart failure (HF), these models are rarely useful for the clinical practitioner as they include multiple variables that might be time-consuming to obtain, they are usually difficult to calculate, and they may suffer from statistical overfitting. To investigate whether a simpler model, namely the ACEF-MDRD score, could be used for predicting one-year mortality in HF patients. 748 cases within the SELFIE-HF registry had complete data to calculate the ACEF-MDRD score. Patients were grouped into tertiles for analyses. For all tests, a p-value <0.05 was accepted as significant. Significantly more patients within the ACEF-MDRD high tertile (30.0%) died within one year, as compared to other tertiles (10.8% and 16.1%, respectively, for ACEF-MDRD low and ACEF-MDRD med , p<0.001 for both comparisons). There was a stepwise decrease in one-year survival as the ACEF-MDRD score increased (log-rank p<0.001). ACEF-MDRD was an independent predictor of survival after adjusting for other variables (OR: 1.14, 95%CI:1.04 - 1.24, p=0.006). ACEF-MDRD score offered similar accuracy to the GWTG-HF score for predicting one-year mortality (p=0.14). ACEF-MDRD is a predictor of mortality in patients with HF, and its usefulness is comparable to similar yet more complicated models.

Exploring First Language Use in Non-Formal Foreign Language Education: A Mixed- Method Approach

In Foreign Language Teaching, investigating the multiple variables that intervene in first language use is essential to uncover learning processes and design optimal teaching practices. Past studies have mainly focused on identifying teachers’ reasons for their first language use, while research on learners’ views is scarce. This mixed-method case study aims to provide a comprehensive description of first language use in a foreign language classroom through the examination of its pedagogical functions and the corresponding views held by the teacher and his students. This article adds to the scholarly body of knowledge about the role of first language use in Foreign Language Teaching with the yielding of constructive insights from non-formal education–a non-previously researched context. Specifically, this study was conducted in “Mar Menor” Center of Adult Education (Southern Spain). The main research questions address the quantification of the pedagogical functions of first language use generated by the instructor in certain classes and the comparison of his views with those of his students, concerning L1 use in general teaching and his sessions. Data comprised classroom observations, teacher’s stimulated recalls, students’ diaries and questionnaires addressed to both sets of participants. The results revealed the multifunctional nature of the teacher’s first language use and a fairly degree of alignment between his views and those of his students. This study underscores the importance of fostering learners’ agency. Furthermore, its findings can potentially inform Foreign Language Teaching by deepening the understanding of the myriad of factors and perspectives involved in first language use.

Factors influencing the 5-year survival rate of oral cancer patients in the Mongolian population: a retrospective cohort study.

The high mortality rate of head and neck cancers, particularly oral cancer, poses a significant health challenge in developing nations such as Mongolia. This retrospective survival analysis study was conducted to identify factors influencing the 5-year survival rate of oral squamous cell carcinoma patients. The study analyzed data from 173 patients diagnosed with oral squamous cell carcinoma, including multiple variables such as age, gender, residence, education, tobacco and alcohol consumption, oral health indicators, family history, precancerous conditions, cancer characteristics, treatment, rehabilitation, cancer recurrence, and 5-year survival. Survival analysis was conducted using the Kaplan-Meier method, and STATA was used for statistical analysis. The study revealed a 5-year survival rate of 50.3% for oral cancer patients, with a survival rate of 38% for tongue cancer patients. Age, residence, cancer stage, and cancer recurrence were identified as significant survival predictors. Compared to those aged 60 or younger, the hazard ratio (HR) for patients aged 61 or older was 1.52. Survival was associated with female gender (HR = 0.47, CI = 0.29-0.77). Urban residence was associated with decreased survival (HR = 1.92, CI = 1.22-3.05). Significantly worse survival was associated with the presence of cancer recurrence (HR = 1.99, CI = 1.15-3.04). Oral cancer patients in stage IV had a fourfold higher risk of mortality compared to those in stage I (HR = 4.08, CI = 1.2-13.84). This research highlights the influence of age, urban habitation, and cancer recurrence on oral cancer survival. Age, urban residence, and cancer recurrence were all associated with decreased survival, whereas cancer at stage IV substantially increased the risk of death. The significance of early detection, treatment, and active surveillance to identify oral cancer at an early stage is highlighted by these findings. Compared to industrialized nations, Mongolia's lower oral cancer survival rates emphasize the need to increase public awareness and education. A comprehensive approach is required to improve oral cancer patient survival rates and quality of life, including emphasizing early detection through active surveillance, implementing preventive measures, and advancing cancer education initiatives.

Performance of the cobas EZH2 mutation test on clinical samples from non-Hodgkin lymphoma patients

Objective To present the technical verification and clinical validation of the companion diagnostic assay, cobas® EZH2 Mutation Test (cobas EZH2 Test), targeting gain-of-function EZH2 mutations in follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL). The focus is on patient clinical samples proving that the test met the performance criteria required for FDA approval of a companion diagnostic test. Design Epizyme, Inc., Eisai Co., Ltd., and Roche Molecular Systems, Inc., collaborated to develop the cobas EZH2 Test on an RT-PCR platform. The assay design needed to detect the gain-of-function EZH2 mutations found in FL and DLBCL indications. Thus, the test was optimized for investigational purposes in a clinical trial setting. Part of its technical verification included testing of patient tumor samples with a documented diagnosis of FL and DLBCL procured from commercial vendors, and the clinical validation used patient samples from the Epizyme clinical study. Both the technical performance verification method correlation study (104 clinical commercially acquired samples) and the clinical validation accuracy study (341 patient samples from the therapeutic study) used next-generation sequencing as a reference method to establish true vs. false results by cobas EZH2 Test. The reproducibility study used a 15-member panel of DNA samples with varying EZH2 mutation status from procured clinical FL and DLBCL patient samples under multiple variables. Results Single and rare, infrequent double EZH2 mutations were detected in FL and DLBCL samples. Agreements between results from cobas EZH2 and sequencing were &gt;98% from commercial clinical samples and from the therapeutic study clinical samples. The reproducibility study obtained 178 to 180 valid results for each panel member, with an overall invalid rate of 0.37%. The agreement for each per panel member was 100%. Conclusion cobas EZH2 Test data demonstrated that the test is reliable and will perform well in a commercial customer environment.